Authors: Bryan Jones*, Baruch College, CUNY
Topics: Population Geography, Quantitative Methods
Keywords: Spatial population projections, global change, United States
Session Type: Paper
Start / End Time: 5:20 PM / 7:00 PM
Room: Napoleon C2, Sheraton 3rd Floor
Presentation File: No File Uploaded
Spatially explicit projections of the human population are of growing importance in the scenario-based assessment of global change, including impacts, adaptation, and vulnerability (IAV) research. Concurrent to shifting global and regional climate characteristics, changes in the size and spatial distribution of the population will have significant ecological and socio-economic implications, particularly in regions experiencing rapid growth and urbanization. Assessing these implications requires plausible alternative projections of spatial population distribution, and there is increasing demand for spatial projections that can be made consistent with widely used global change narratives describing changes in other characteristics of society that could affect spatial outcomes. Similarly, estimates of both the size and spatial orientation of populations are crucial to planners that must ensure adequate access to public services while attempting to reduce vulnerability to climate-related hazards. Understanding the implications of spatial population change on exposure to climate-related hazards is a key component of characterizing vulnerability. Here we present 80-year spatial population scenarios (2020-2100) consistent with the development narrative corresponding to each of the five shared socioeconomic pathways. Building from previous gravity-based downscaling models, we present a gravity-based econometric approach in which spatial patterns of change are driven by the relationship between historic population movement and demographic characteristics of the population. Projections are produced on a 7.5’ global grid in 10-year intervals for the United States, and the model is calibrated to observed urban and rural population change.